Defining data types and knowing the schema of your data has always been a crucial factor for performant data platforms, especially when it comes to string datatypes which can potentially consume a lot of space and memory. For Lakehouses in general (not only Fabric Lakehouses), there is usually only one data type for text data which is a generic STRING of an arbitrary length. In terms of Apache Spark, this is StringType(). While this applies to Spark dataframes, this is not entirely true for Spark tables – here is what the docs say:
Read through for more information on that, as well as how to define a table in a Microsoft Fabric lakehouse using VARCHAR(). The display is a little weird, but Greg Low explains why in the comments.